Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations6941
Missing cells43971
Missing cells (%)48.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory705.1 KiB
Average record size in memory104.0 B

Variable types

Text3
Numeric4
Boolean3
Categorical3

Alerts

preventTargetGapPoints has constant value "True"Constant
userFlaggedNewItem has constant value "True"Constant
finalPrice is highly overall correlated with itemPrice and 2 other fieldsHigh correlation
itemPrice is highly overall correlated with finalPrice and 2 other fieldsHigh correlation
needsFetchReview is highly overall correlated with userFlaggedBarcode and 1 other fieldsHigh correlation
partnerItemId is highly overall correlated with userFlaggedBarcodeHigh correlation
quantityPurchased is highly overall correlated with userFlaggedQuantityHigh correlation
userFlaggedBarcode is highly overall correlated with finalPrice and 5 other fieldsHigh correlation
userFlaggedPrice is highly overall correlated with finalPrice and 3 other fieldsHigh correlation
userFlaggedQuantity is highly overall correlated with quantityPurchased and 1 other fieldsHigh correlation
barcode has 3851 (55.5%) missing valuesMissing
description has 381 (5.5%) missing valuesMissing
finalPrice has 174 (2.5%) missing valuesMissing
itemPrice has 174 (2.5%) missing valuesMissing
needsFetchReview has 6128 (88.3%) missing valuesMissing
preventTargetGapPoints has 6583 (94.8%) missing valuesMissing
quantityPurchased has 174 (2.5%) missing valuesMissing
userFlaggedBarcode has 6604 (95.1%) missing valuesMissing
userFlaggedNewItem has 6618 (95.3%) missing valuesMissing
userFlaggedPrice has 6642 (95.7%) missing valuesMissing
userFlaggedQuantity has 6642 (95.7%) missing valuesMissing
partnerItemId has 145 (2.1%) zerosZeros

Reproduction

Analysis started2024-10-09 04:08:32.926241
Analysis finished2024-10-09 04:10:20.818768
Duration1 minute and 47.89 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Distinct679
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size54.4 KiB
2024-10-09T04:10:21.270134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters166584
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique377 ?
Unique (%)5.4%

Sample

1st row5ff1e1eb0a720f0523000575
2nd row5ff1e1bb0a720f052300056b
3rd row5ff1e1bb0a720f052300056b
4th row5ff1e1f10a720f052300057a
5th row5ff1e1ee0a7214ada100056f
ValueCountFrequency (%)
600f2fc80a720f0535000030 459
 
6.6%
600f39c30a7214ada2000030 450
 
6.5%
600f24970a720f053500002f 381
 
5.5%
600f0cc70a720f053500002c 217
 
3.1%
600a1a8d0a7214ada2000008 203
 
2.9%
60049d9d0a720f05f3000094 194
 
2.8%
60025cb80a720f05f300008d 185
 
2.7%
600260210a720f05f300008f 183
 
2.6%
600a1e270a720f0535000009 176
 
2.5%
600edb570a720f053500001d 155
 
2.2%
Other values (669) 4338
62.5%
2024-10-09T04:10:22.411957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 58962
35.4%
a 14693
 
8.8%
2 13346
 
8.0%
f 11252
 
6.8%
7 9412
 
5.7%
5 8794
 
5.3%
3 7813
 
4.7%
6 7802
 
4.7%
1 6020
 
3.6%
4 5640
 
3.4%
Other values (6) 22850
 
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 166584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 58962
35.4%
a 14693
 
8.8%
2 13346
 
8.0%
f 11252
 
6.8%
7 9412
 
5.7%
5 8794
 
5.3%
3 7813
 
4.7%
6 7802
 
4.7%
1 6020
 
3.6%
4 5640
 
3.4%
Other values (6) 22850
 
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 166584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 58962
35.4%
a 14693
 
8.8%
2 13346
 
8.0%
f 11252
 
6.8%
7 9412
 
5.7%
5 8794
 
5.3%
3 7813
 
4.7%
6 7802
 
4.7%
1 6020
 
3.6%
4 5640
 
3.4%
Other values (6) 22850
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 166584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 58962
35.4%
a 14693
 
8.8%
2 13346
 
8.0%
f 11252
 
6.8%
7 9412
 
5.7%
5 8794
 
5.3%
3 7813
 
4.7%
6 7802
 
4.7%
1 6020
 
3.6%
4 5640
 
3.4%
Other values (6) 22850
 
13.7%

barcode
Text

MISSING 

Distinct568
Distinct (%)18.4%
Missing3851
Missing (%)55.5%
Memory size54.4 KiB
2024-10-09T04:10:22.865206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.067314
Min length2

Characters and Unicode

Total characters34198
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique261 ?
Unique (%)8.4%

Sample

1st row4011
2nd row4011
3rd row028400642255
4th row4011
5th row4011
ValueCountFrequency (%)
4011 177
 
5.7%
036000320893 92
 
3.0%
034100573065 90
 
2.9%
036000391718 87
 
2.8%
012000809941 76
 
2.5%
076840580750 63
 
2.0%
041000022623 54
 
1.7%
076840100354 53
 
1.7%
028400642033 45
 
1.5%
311111511867 41
 
1.3%
Other values (558) 2312
74.8%
2024-10-09T04:10:23.579945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11212
32.8%
1 4353
 
12.7%
4 3087
 
9.0%
3 2690
 
7.9%
2 2582
 
7.6%
5 2310
 
6.8%
7 2083
 
6.1%
6 2026
 
5.9%
8 1890
 
5.5%
9 1471
 
4.3%
Other values (14) 494
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34198
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11212
32.8%
1 4353
 
12.7%
4 3087
 
9.0%
3 2690
 
7.9%
2 2582
 
7.6%
5 2310
 
6.8%
7 2083
 
6.1%
6 2026
 
5.9%
8 1890
 
5.5%
9 1471
 
4.3%
Other values (14) 494
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34198
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11212
32.8%
1 4353
 
12.7%
4 3087
 
9.0%
3 2690
 
7.9%
2 2582
 
7.6%
5 2310
 
6.8%
7 2083
 
6.1%
6 2026
 
5.9%
8 1890
 
5.5%
9 1471
 
4.3%
Other values (14) 494
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34198
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11212
32.8%
1 4353
 
12.7%
4 3087
 
9.0%
3 2690
 
7.9%
2 2582
 
7.6%
5 2310
 
6.8%
7 2083
 
6.1%
6 2026
 
5.9%
8 1890
 
5.5%
9 1471
 
4.3%
Other values (14) 494
 
1.4%

description
Text

MISSING 

Distinct1889
Distinct (%)28.8%
Missing381
Missing (%)5.5%
Memory size54.4 KiB
2024-10-09T04:10:24.108380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length155
Median length92
Mean length29.15122
Min length2

Characters and Unicode

Total characters191232
Distinct characters83
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1136 ?
Unique (%)17.3%

Sample

1st rowITEM NOT FOUND
2nd rowITEM NOT FOUND
3rd rowDORITOS TORTILLA CHIP SPICY SWEET CHILI REDUCED FAT BAG 1 OZ
4th rowITEM NOT FOUND
5th rowITEM NOT FOUND
ValueCountFrequency (%)
oz 1209
 
3.5%
931
 
2.7%
cheese 327
 
1.0%
12 321
 
0.9%
bag 276
 
0.8%
hyv 246
 
0.7%
can 241
 
0.7%
ct 237
 
0.7%
regular 215
 
0.6%
fl 211
 
0.6%
Other values (3183) 30149
87.7%
2024-10-09T04:10:25.032173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27810
 
14.5%
E 8222
 
4.3%
e 7865
 
4.1%
R 7158
 
3.7%
C 6724
 
3.5%
S 6612
 
3.5%
A 6575
 
3.4%
L 6297
 
3.3%
O 6266
 
3.3%
N 5520
 
2.9%
Other values (73) 102183
53.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 191232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
27810
 
14.5%
E 8222
 
4.3%
e 7865
 
4.1%
R 7158
 
3.7%
C 6724
 
3.5%
S 6612
 
3.5%
A 6575
 
3.4%
L 6297
 
3.3%
O 6266
 
3.3%
N 5520
 
2.9%
Other values (73) 102183
53.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 191232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
27810
 
14.5%
E 8222
 
4.3%
e 7865
 
4.1%
R 7158
 
3.7%
C 6724
 
3.5%
S 6612
 
3.5%
A 6575
 
3.4%
L 6297
 
3.3%
O 6266
 
3.3%
N 5520
 
2.9%
Other values (73) 102183
53.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 191232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
27810
 
14.5%
E 8222
 
4.3%
e 7865
 
4.1%
R 7158
 
3.7%
C 6724
 
3.5%
S 6612
 
3.5%
A 6575
 
3.4%
L 6297
 
3.3%
O 6266
 
3.3%
N 5520
 
2.9%
Other values (73) 102183
53.4%

finalPrice
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct823
Distinct (%)12.2%
Missing174
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean7.871661
Minimum0
Maximum441.58
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size54.4 KiB
2024-10-09T04:10:25.399378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.56
Q12.29
median4.28
Q39.99
95-th percentile26
Maximum441.58
Range441.58
Interquartile range (IQR)7.7

Descriptive statistics

Standard deviation14.656776
Coefficient of variation (CV)1.8619674
Kurtosis207.38946
Mean7.871661
Median Absolute Deviation (MAD)2.7
Skewness11.383034
Sum53267.53
Variance214.82108
MonotonicityNot monotonic
2024-10-09T04:10:25.706926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 375
 
5.4%
9.99 355
 
5.1%
3.99 243
 
3.5%
4.99 195
 
2.8%
0.56 190
 
2.7%
2.99 179
 
2.6%
5.99 176
 
2.5%
3.49 139
 
2.0%
2.34 134
 
1.9%
5 124
 
1.8%
Other values (813) 4657
67.1%
(Missing) 174
 
2.5%
ValueCountFrequency (%)
0 4
 
0.1%
0.16 1
 
< 0.1%
0.19 13
 
0.2%
0.25 2
 
< 0.1%
0.32 2
 
< 0.1%
0.48 3
 
< 0.1%
0.5 76
 
1.1%
0.54 66
 
1.0%
0.55 2
 
< 0.1%
0.56 190
2.7%
ValueCountFrequency (%)
441.58 1
 
< 0.1%
245 3
< 0.1%
223.36 5
0.1%
180 6
0.1%
168.84 5
0.1%
115.96 1
 
< 0.1%
100.48 1
 
< 0.1%
100 6
0.1%
95.84 4
0.1%
82.34 1
 
< 0.1%

itemPrice
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct823
Distinct (%)12.2%
Missing174
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean7.8721782
Minimum0
Maximum441.58
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size54.4 KiB
2024-10-09T04:10:26.043613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.56
Q12.29
median4.28
Q39.99
95-th percentile26
Maximum441.58
Range441.58
Interquartile range (IQR)7.7

Descriptive statistics

Standard deviation14.656623
Coefficient of variation (CV)1.8618256
Kurtosis207.39662
Mean7.8721782
Median Absolute Deviation (MAD)2.7
Skewness11.383294
Sum53271.03
Variance214.8166
MonotonicityNot monotonic
2024-10-09T04:10:26.348511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 375
 
5.4%
9.99 355
 
5.1%
3.99 243
 
3.5%
4.99 196
 
2.8%
0.56 190
 
2.7%
2.99 180
 
2.6%
5.99 176
 
2.5%
3.49 139
 
2.0%
2.34 134
 
1.9%
5 124
 
1.8%
Other values (813) 4655
67.1%
(Missing) 174
 
2.5%
ValueCountFrequency (%)
0 4
 
0.1%
0.16 1
 
< 0.1%
0.19 13
 
0.2%
0.25 2
 
< 0.1%
0.32 2
 
< 0.1%
0.48 3
 
< 0.1%
0.5 76
 
1.1%
0.54 66
 
1.0%
0.55 2
 
< 0.1%
0.56 190
2.7%
ValueCountFrequency (%)
441.58 1
 
< 0.1%
245 3
< 0.1%
223.36 5
0.1%
180 6
0.1%
168.84 5
0.1%
115.96 1
 
< 0.1%
100.48 1
 
< 0.1%
100 6
0.1%
95.84 4
0.1%
82.34 1
 
< 0.1%

needsFetchReview
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.2%
Missing6128
Missing (%)88.3%
Memory size54.4 KiB
False
 
594
True
 
219
(Missing)
6128 
ValueCountFrequency (%)
False 594
 
8.6%
True 219
 
3.2%
(Missing) 6128
88.3%
2024-10-09T04:10:26.640359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

partnerItemId
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct916
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean988.52428
Minimum0
Maximum2043
Zeros145
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size54.4 KiB
2024-10-09T04:10:26.912248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11027
median1143
Q31274
95-th percentile1644
Maximum2043
Range2043
Interquartile range (IQR)247

Descriptive statistics

Standard deviation527.38082
Coefficient of variation (CV)0.53350316
Kurtosis-0.13640789
Mean988.52428
Median Absolute Deviation (MAD)123
Skewness-1.0174858
Sum6861347
Variance278130.53
MonotonicityNot monotonic
2024-10-09T04:10:27.232023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 531
 
7.7%
2 203
 
2.9%
0 145
 
2.1%
3 135
 
1.9%
4 113
 
1.6%
5 106
 
1.5%
6 34
 
0.5%
7 34
 
0.5%
8 34
 
0.5%
9 34
 
0.5%
Other values (906) 5572
80.3%
ValueCountFrequency (%)
0 145
 
2.1%
1 531
7.7%
2 203
 
2.9%
3 135
 
1.9%
4 113
 
1.6%
5 106
 
1.5%
6 34
 
0.5%
7 34
 
0.5%
8 34
 
0.5%
9 34
 
0.5%
ValueCountFrequency (%)
2043 1
< 0.1%
2040 1
< 0.1%
2036 1
< 0.1%
2033 1
< 0.1%
2029 1
< 0.1%
2026 1
< 0.1%
1986 1
< 0.1%
1983 1
< 0.1%
1980 1
< 0.1%
1976 1
< 0.1%

preventTargetGapPoints
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing6583
Missing (%)94.8%
Memory size54.4 KiB
True
 
358
(Missing)
6583 
ValueCountFrequency (%)
True 358
 
5.2%
(Missing) 6583
94.8%
2024-10-09T04:10:27.537566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

quantityPurchased
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)0.2%
Missing174
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean1.3861386
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.4 KiB
2024-10-09T04:10:27.717968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile4
Maximum17
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2043632
Coefficient of variation (CV)0.86886201
Kurtosis36.002117
Mean1.3861386
Median Absolute Deviation (MAD)0
Skewness5.1137362
Sum9380
Variance1.4504907
MonotonicityNot monotonic
2024-10-09T04:10:27.955365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 5628
81.1%
2 622
 
9.0%
4 170
 
2.4%
3 134
 
1.9%
5 101
 
1.5%
6 37
 
0.5%
8 22
 
0.3%
10 15
 
0.2%
7 13
 
0.2%
9 13
 
0.2%
Other values (3) 12
 
0.2%
(Missing) 174
 
2.5%
ValueCountFrequency (%)
1 5628
81.1%
2 622
 
9.0%
3 134
 
1.9%
4 170
 
2.4%
5 101
 
1.5%
6 37
 
0.5%
7 13
 
0.2%
8 22
 
0.3%
9 13
 
0.2%
10 15
 
0.2%
ValueCountFrequency (%)
17 3
 
< 0.1%
14 3
 
< 0.1%
12 6
 
0.1%
10 15
 
0.2%
9 13
 
0.2%
8 22
 
0.3%
7 13
 
0.2%
6 37
 
0.5%
5 101
1.5%
4 170
2.4%

userFlaggedBarcode
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)1.8%
Missing6604
Missing (%)95.1%
Memory size54.4 KiB
034100573065
166 
4011
107 
1234
32 
028400642255
 
13
079400066619
 
10

Length

Max length12
Median length12
Mean length8.7002967
Min length4

Characters and Unicode

Total characters2932
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4011
2nd row028400642255
3rd row4011
4th row4011
5th row1234

Common Values

ValueCountFrequency (%)
034100573065 166
 
2.4%
4011 107
 
1.5%
1234 32
 
0.5%
028400642255 13
 
0.2%
079400066619 10
 
0.1%
075925306254 9
 
0.1%
(Missing) 6604
95.1%

Length

2024-10-09T04:10:28.246749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-09T04:10:28.567859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
034100573065 166
49.3%
4011 107
31.8%
1234 32
 
9.5%
028400642255 13
 
3.9%
079400066619 10
 
3.0%
075925306254 9
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 868
29.6%
1 422
14.4%
5 385
13.1%
3 373
12.7%
4 350
11.9%
6 218
 
7.4%
7 185
 
6.3%
2 89
 
3.0%
9 29
 
1.0%
8 13
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2932
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 868
29.6%
1 422
14.4%
5 385
13.1%
3 373
12.7%
4 350
11.9%
6 218
 
7.4%
7 185
 
6.3%
2 89
 
3.0%
9 29
 
1.0%
8 13
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2932
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 868
29.6%
1 422
14.4%
5 385
13.1%
3 373
12.7%
4 350
11.9%
6 218
 
7.4%
7 185
 
6.3%
2 89
 
3.0%
9 29
 
1.0%
8 13
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2932
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 868
29.6%
1 422
14.4%
5 385
13.1%
3 373
12.7%
4 350
11.9%
6 218
 
7.4%
7 185
 
6.3%
2 89
 
3.0%
9 29
 
1.0%
8 13
 
0.4%

userFlaggedNewItem
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing6618
Missing (%)95.3%
Memory size54.4 KiB
True
 
323
(Missing)
6618 
ValueCountFrequency (%)
True 323
 
4.7%
(Missing) 6618
95.3%
2024-10-09T04:10:28.868524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

userFlaggedPrice
Categorical

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)4.3%
Missing6642
Missing (%)95.7%
Memory size54.4 KiB
29.00
142 
1.00
26 
10.00
22 
28.00
15 
20.00
 
14
Other values (8)
80 

Length

Max length5
Median length5
Mean length4.8862876
Min length4

Characters and Unicode

Total characters1461
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26.00
2nd row10.00
3rd row26.00
4th row28.00
5th row2.56

Common Values

ValueCountFrequency (%)
29.00 142
 
2.0%
1.00 26
 
0.4%
10.00 22
 
0.3%
28.00 15
 
0.2%
20.00 14
 
0.2%
21.00 14
 
0.2%
27.00 13
 
0.2%
26.00 10
 
0.1%
25.00 10
 
0.1%
23.00 10
 
0.1%
Other values (3) 23
 
0.3%
(Missing) 6642
95.7%

Length

2024-10-09T04:10:29.096912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
29.00 142
47.5%
1.00 26
 
8.7%
10.00 22
 
7.4%
28.00 15
 
5.0%
20.00 14
 
4.7%
21.00 14
 
4.7%
27.00 13
 
4.3%
26.00 10
 
3.3%
25.00 10
 
3.3%
23.00 10
 
3.3%
Other values (3) 23
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 618
42.3%
. 299
20.5%
2 260
17.8%
9 142
 
9.7%
1 62
 
4.2%
6 18
 
1.2%
5 18
 
1.2%
8 15
 
1.0%
7 13
 
0.9%
3 10
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1461
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 618
42.3%
. 299
20.5%
2 260
17.8%
9 142
 
9.7%
1 62
 
4.2%
6 18
 
1.2%
5 18
 
1.2%
8 15
 
1.0%
7 13
 
0.9%
3 10
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1461
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 618
42.3%
. 299
20.5%
2 260
17.8%
9 142
 
9.7%
1 62
 
4.2%
6 18
 
1.2%
5 18
 
1.2%
8 15
 
1.0%
7 13
 
0.9%
3 10
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1461
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 618
42.3%
. 299
20.5%
2 260
17.8%
9 142
 
9.7%
1 62
 
4.2%
6 18
 
1.2%
5 18
 
1.2%
8 15
 
1.0%
7 13
 
0.9%
3 10
 
0.7%

userFlaggedQuantity
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)1.7%
Missing6642
Missing (%)95.7%
Memory size54.4 KiB
1.0
189 
3.0
31 
4.0
30 
2.0
29 
5.0
20 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters897
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0 189
 
2.7%
3.0 31
 
0.4%
4.0 30
 
0.4%
2.0 29
 
0.4%
5.0 20
 
0.3%
(Missing) 6642
95.7%

Length

2024-10-09T04:10:29.350198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-09T04:10:29.647171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 189
63.2%
3.0 31
 
10.4%
4.0 30
 
10.0%
2.0 29
 
9.7%
5.0 20
 
6.7%

Most occurring characters

ValueCountFrequency (%)
. 299
33.3%
0 299
33.3%
1 189
21.1%
3 31
 
3.5%
4 30
 
3.3%
2 29
 
3.2%
5 20
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 897
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 299
33.3%
0 299
33.3%
1 189
21.1%
3 31
 
3.5%
4 30
 
3.3%
2 29
 
3.2%
5 20
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 897
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 299
33.3%
0 299
33.3%
1 189
21.1%
3 31
 
3.5%
4 30
 
3.3%
2 29
 
3.2%
5 20
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 897
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 299
33.3%
0 299
33.3%
1 189
21.1%
3 31
 
3.5%
4 30
 
3.3%
2 29
 
3.2%
5 20
 
2.2%

Interactions

2024-10-09T04:10:00.741634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-09T04:08:34.643614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-09T04:09:02.259027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-09T04:09:29.104894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-09T04:10:07.405132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-09T04:08:41.089464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-09T04:09:08.691236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-09T04:09:38.135774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-09T04:10:11.853799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-09T04:08:49.150970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-09T04:09:16.780546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-09T04:09:46.623993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-09T04:10:18.232059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-09T04:08:58.439627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-09T04:09:26.142649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-09T04:09:57.013695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-10-09T04:10:29.892247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
finalPriceitemPriceneedsFetchReviewpartnerItemIdquantityPurchaseduserFlaggedBarcodeuserFlaggedPriceuserFlaggedQuantity
finalPrice1.0001.0000.479-0.1030.3980.8300.9430.432
itemPrice1.0001.0000.479-0.1030.3980.8300.9430.432
needsFetchReview0.4790.4791.0000.3800.2190.7130.5530.481
partnerItemId-0.103-0.1030.3801.0000.1500.5440.3450.371
quantityPurchased0.3980.3980.2190.1501.0000.3180.3570.701
userFlaggedBarcode0.8300.8300.7130.5440.3181.0000.8020.520
userFlaggedPrice0.9430.9430.5530.3450.3570.8021.0000.479
userFlaggedQuantity0.4320.4320.4810.3710.7010.5200.4791.000

Missing values

2024-10-09T04:10:18.878180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-09T04:10:19.598240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-09T04:10:20.304444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

receiptIdbarcodedescriptionfinalPriceitemPriceneedsFetchReviewpartnerItemIdpreventTargetGapPointsquantityPurchaseduserFlaggedBarcodeuserFlaggedNewItemuserFlaggedPriceuserFlaggedQuantity
05ff1e1eb0a720f05230005754011ITEM NOT FOUND26.0026.00False1True5.04011True26.005.0
15ff1e1bb0a720f052300056b4011ITEM NOT FOUND11NaN1NaN1.0NaNNaNNaNNaN
25ff1e1bb0a720f052300056b028400642255DORITOS TORTILLA CHIP SPICY SWEET CHILI REDUCED FAT BAG 1 OZ10.0010.00True2True1.0028400642255True10.001.0
35ff1e1f10a720f052300057aNaNNaNNaNNaNFalse1TrueNaN4011True26.003.0
45ff1e1ee0a7214ada100056f4011ITEM NOT FOUND28.0028.00False1True4.04011True28.004.0
55ff1e1d20a7214ada10005614011ITEM NOT FOUND11NaN1NaN1.0NaNNaNNaNNaN
65ff1e1d20a7214ada10005611234NaN2.562.56True2True3.01234True2.563.0
75ff1e1e40a7214ada10005664011ITEM NOT FOUND3.253.25False1True1.04011NaNNaNNaN
85ff1e1cd0a720f052300056fNaNMSSN TORTLLA2.232.23NaN1009NaN1.0NaNNaNNaNNaN
95ff1e1a40a720f0523000569046000832517Old El Paso Mild Chopped Green Chiles, 4.5 Oz10.0010.00NaN0NaN1.0NaNNaNNaNNaN
receiptIdbarcodedescriptionfinalPriceitemPriceneedsFetchReviewpartnerItemIdpreventTargetGapPointsquantityPurchaseduserFlaggedBarcodeuserFlaggedNewItemuserFlaggedPriceuserFlaggedQuantity
6931603c7c6c0a7217c72c0003b3B076FJ92M4mueller austria hypergrind precision electric spice/coffee grinder millwith large grinding capacity and hd motor also for spices, herbs, nuts,grains, white22.9722.97NaN0NaN1.0NaNNaNNaNNaN
6932603c7c6c0a7217c72c0003b3B07BRRLSVCthindust summer face mask - sun protection neck gaiter for outdooractivities11.9911.99NaN1NaN1.0NaNNaNNaNNaN
6933603c3d240a720fde10000373B076FJ92M4mueller austria hypergrind precision electric spice/coffee grinder millwith large grinding capacity and hd motor also for spices, herbs, nuts,grains, white22.9722.97NaN0NaN1.0NaNNaNNaNNaN
6934603c3d240a720fde10000373B07BRRLSVCthindust summer face mask - sun protection neck gaiter for outdooractivities11.9911.99NaN1NaN1.0NaNNaNNaNNaN
6935603cc2bc0a720fde100003e9B076FJ92M4mueller austria hypergrind precision electric spice/coffee grinder millwith large grinding capacity and hd motor also for spices, herbs, nuts,grains, white22.9722.97NaN0NaN1.0NaNNaNNaNNaN
6936603cc2bc0a720fde100003e9B07BRRLSVCthindust summer face mask - sun protection neck gaiter for outdooractivities11.9911.99NaN1NaN1.0NaNNaNNaNNaN
6937603cc0630a720fde100003e6B076FJ92M4mueller austria hypergrind precision electric spice/coffee grinder millwith large grinding capacity and hd motor also for spices, herbs, nuts,grains, white22.9722.97NaN0NaN1.0NaNNaNNaNNaN
6938603cc0630a720fde100003e6B07BRRLSVCthindust summer face mask - sun protection neck gaiter for outdooractivities11.9911.99NaN1NaN1.0NaNNaNNaNNaN
6939603ce7100a7217c72c000405B076FJ92M4mueller austria hypergrind precision electric spice/coffee grinder millwith large grinding capacity and hd motor also for spices, herbs, nuts,grains, white22.9722.97NaN0NaN1.0NaNNaNNaNNaN
6940603ce7100a7217c72c000405B07BRRLSVCthindust summer face mask - sun protection neck gaiter for outdooractivities11.9911.99NaN1NaN1.0NaNNaNNaNNaN